sklearn.cluster.MiniBatchKMeans

class sklearn.cluster.MiniBatchKMeans(n_clusters=8, *, init='k-means++', max_iter=100, batch_size=1024, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01)

Mini-Batch K-Means clustering.

Read more in the User Guide.

Parameters

n_clustersint, default=8

The number of clusters to form as well as the number of centroids to generate.

init{‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’

Method for initialization:

‘k-means++’ : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.

‘random’: choose n_clusters observations (rows) at random from data for the initial centroids.

If an array is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.

max_iterint, default=100

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics.

batch_sizeint, default=1024

Size of the mini batches. For faster compuations, you can set the batch_size greater than 256 * number of cores to enable parallelism on all cores.

Changed in version 1.0: batch_size default changed from 100 to 1024.

verboseint, default=0

Verbosity mode.

compute_labelsbool, default=True

Compute label assignment and inertia for the complete dataset once the minibatch optimization has converged in fit.

random_stateint, RandomState instance or None, default=None

Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic. See Glossary.

tolfloat, default=0.0

Control early stopping based on the relative center changes as measured by a smoothed, variance-normalized of the mean center squared position changes. This early stopping heuristics is closer to the one used for the batch variant of the algorithms but induces a slight computational and memory overhead over the inertia heuristic.

To disable convergence detection based on normalized center change, set tol to 0.0 (default).

max_no_improvementint, default=10

Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia.

To disable convergence detection based on inertia, set max_no_improvement to None.

init_sizeint, default=None

Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters.

If None, the heuristic is init_size = 3 * batch_size if 3 * batch_size < n_clusters, else init_size = 3 * n_clusters.

n_initint, default=3

Number of random initializations that are tried. In contrast to KMeans, the algorithm is only run once, using the best of the n_init initializations as measured by inertia.

reassignment_ratiofloat, default=0.01

Control the fraction of the maximum number of counts for a center to be reassigned. A higher value means that low count centers are more easily reassigned, which means that the model will take longer to converge, but should converge in a better clustering. However, too high a value may cause convergence issues, especially with a small batch size.

Attributes

cluster_centers_ndarray of shape (n_clusters, n_features)

Coordinates of cluster centers.

labels_ndarray of shape (n_samples,)

Labels of each point (if compute_labels is set to True).

inertia_float

The value of the inertia criterion associated with the chosen partition if compute_labels is set to True. If compute_labels is set to False, it’s an approximation of the inertia based on an exponentially weighted average of the batch inertiae. The inertia is defined as the sum of square distances of samples to their cluster center, weighted by the sample weights if provided.

n_iter_int

Number of iterations over the full dataset.

n_steps_int

Number of minibatches processed.

New in version 1.0.

counts_ndarray of shape (n_clusters,)

DEPRECATED: The attribute counts_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

init_size_int

DEPRECATED: The attribute init_size_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

KMeans

The classic implementation of the clustering method based on the Lloyd’s algorithm. It consumes the whole set of input data at each iteration.

Notes

See https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf

Examples

>>> from sklearn.cluster import MiniBatchKMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [4, 2], [4, 0], [4, 4],
...               [4, 5], [0, 1], [2, 2],
...               [3, 2], [5, 5], [1, -1]])
>>> # manually fit on batches
>>> kmeans = MiniBatchKMeans(n_clusters=2,
...                          random_state=0,
...                          batch_size=6)
>>> kmeans = kmeans.partial_fit(X[0:6,:])
>>> kmeans = kmeans.partial_fit(X[6:12,:])
>>> kmeans.cluster_centers_
array([[2. , 1. ],
       [3.5, 4.5]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
>>> # fit on the whole data
>>> kmeans = MiniBatchKMeans(n_clusters=2,
...                          random_state=0,
...                          batch_size=6,
...                          max_iter=10).fit(X)
>>> kmeans.cluster_centers_
array([[1.19..., 1.22...],
       [4.03..., 2.46...]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)

Methods

fit(X[, y, sample_weight])	Compute the centroids on X by chunking it into mini-batches.
fit_predict(X[, y, sample_weight])	Compute cluster centers and predict cluster index for each sample.
fit_transform(X[, y, sample_weight])	Compute clustering and transform X to cluster-distance space.
get_params([deep])	Get parameters for this estimator.
partial_fit(X[, y, sample_weight])	Update k means estimate on a single mini-batch X.
predict(X[, sample_weight])	Predict the closest cluster each sample in X belongs to.
score(X[, y, sample_weight])	Opposite of the value of X on the K-means objective.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform X to a cluster-distance space.

property counts_

DEPRECATED: The attribute counts_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

fit(X, y=None, sample_weight=None)

Compute the centroids on X by chunking it into mini-batches.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

yIgnored

Not used, present here for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

New in version 0.20.

Returns

selfobject

Fitted estimator.

fit_predict(X, y=None, sample_weight=None)

Compute cluster centers and predict cluster index for each sample.

Convenience method; equivalent to calling fit(X) followed by predict(X).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

yIgnored

Not used, present here for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

Returns

labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

fit_transform(X, y=None, sample_weight=None)

Compute clustering and transform X to cluster-distance space.

Equivalent to fit(X).transform(X), but more efficiently implemented.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

yIgnored

Not used, present here for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

Returns

X_newndarray of shape (n_samples, n_clusters)

X transformed in the new space.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

property init_size_

DEPRECATED: The attribute init_size_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

partial_fit(X, y=None, sample_weight=None)

Update k means estimate on a single mini-batch X.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

yIgnored

Not used, present here for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

Returns

selfobject

Return updated estimator.

predict(X, sample_weight=None)

Predict the closest cluster each sample in X belongs to.

In the vector quantization literature, cluster_centers_ is called the code book and each value returned by predict is the index of the closest code in the code book.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to predict.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

Returns

labelsndarray of shape (n_samples,)

Index of the cluster each sample belongs to.

property random_state_

DEPRECATED: The attribute random_state_ is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26).

score(X, y=None, sample_weight=None)

Opposite of the value of X on the K-means objective.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

New data.

yIgnored

Not used, present here for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

The weights for each observation in X. If None, all observations are assigned equal weight.

Returns

scorefloat

Opposite of the value of X on the K-means objective.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Transform X to a cluster-distance space.

In the new space, each dimension is the distance to the cluster centers. Note that even if X is sparse, the array returned by transform will typically be dense.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

New data to transform.

Returns

X_newndarray of shape (n_samples, n_clusters)

X transformed in the new space.

Examples using sklearn.cluster.MiniBatchKMeans

Biclustering documents with the Spectral Co-clustering algorithm

Online learning of a dictionary of parts of faces

Compare BIRCH and MiniBatchKMeans

Empirical evaluation of the impact of k-means initialization

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparing different clustering algorithms on toy datasets

Faces dataset decompositions

Clustering text documents using k-means